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Numerous techniques with different levels of intrusion and accuracy have been proposed for in situ efficiency estimation. Among them, optimization-based techniques and the air-gap torque (AGT) method show promise when unbalanced supply conditions exist. In this paper, an optimization-based algorithm is proposed for in situ efficiency estimation of induction machines operating with over-/undervoltage and unbalanced supplies. In addition, a comprehensive study is done on the functionality and accuracy of the nonintrusive AGT method which is claimed to be one of the most promising methods in the literature. It is shown that the efficiency calculated by this method under field conditions cannot be used in the decision making process on replacement of the existing machines as well as the relevant calculations regarding the payback period. The research is supported by experimental results on two different induction machines. [ABSTRACT FROM AUTHOR]

A computational problem that arises frequently in computer vision is that of estimating the parameters of a model from data that have been contaminated by noise and outliers. More generally, any practical system that seeks to estimate quantities from noisy data measurements must have at its core some means of dealing with data contamination. The random sample consensus (RANSAC) algorithm is one of the most popular tools for robust estimation. Recent years have seen an explosion of activity in this area, leading to the development of a number of techniques that improve upon the efficiency and robustness of the basic RANSAC algorithm. In this paper, we present a comprehensive overview of recent research in RANSAC-based robust estimation by analyzing and comparing various approaches that have been explored over the years. We provide a common context for this analysis by introducing a new framework for robust estimation, which we call Universal RANSAC (USAC). USAC extends the simple hypothesize-and-verify structure of standard RANSAC to incorporate a number of important practical and computational considerations. In addition, we provide a general-purpose C++ software library that implements the USAC framework by leveraging state-of-the-art algorithms for the various modules. This implementation thus addresses many of the limitations of standard RANSAC within a single unified package. We benchmark the performance of the algorithm on a large collection of estimation problems. The implementation we provide can be used by researchers either as a stand-alone tool for robust estimation or as a benchmark for evaluating new techniques. [ABSTRACT FROM PUBLISHER]

The development of an in situ efficiency estimation technique is a challenging task where the lowest level of intrusion and the highest possible accuracy are required. In this paper, a new algorithm is discussed for the in situ efficiency estimation of induction machines under unbalanced power supplies. Prior work in the literature has concentrated on balanced supplies. In addition, to have a nonintrusive speed measurement, a specific adaptive nonlinear algorithm is applied for the extraction of the speed-dependent current harmonics from the measured current signal. A similar algorithm is used to extract the symmetrical components from the current and voltage signals to handle the unbalanced supply conditions. Experimental results with two different machines are used to prove the effectiveness and generality of the proposed method. Measurement error analysis, as well as repeatability tests, has been done to determine the credibility of the proposed method. [ABSTRACT FROM AUTHOR]

We investigate the performance of distributed least-mean square (LMS) algorithms for parameter estimation over sensor networks where the regression data of each node are corrupted by white measurement noise. Under this condition, we show that the estimates produced by distributed LMS algorithms will be biased if the regression noise is excluded from consideration. We propose a bias-elimination technique and develop a novel class of diffusion LMS algorithms that can mitigate the effect of regression noise and obtain an unbiased estimate of the unknown parameter vector over the network. In our development, we first assume that the variances of the regression noises are known a priori. Later, we relax this assumption by estimating these variances in real time. We analyze the stability and convergence of the proposed algorithms and derive closed-form expressions to characterize their mean-square error performance in transient and steady-state regimes. We further provide computer experiment results that illustrate the efficiency of the proposed algorithms and support the analytical findings. [ABSTRACT FROM AUTHOR]